Mouse and Human cell recovery

A 10x genomics scRNA-Seq library was constructed from a mix of 293T and NIH3T3 cells. A mouse and a human cell barcode was selected for pulldown with a biotinylated DNA oligo with LNA bases added every third nucleotide.

Following reamplification the 2 cell libraries were pooled and resequenced together. The raw fastqs were then processed using a Snakemake pipeline, to produce two processed data files:

  1. A matrix with UMIs per cell (column) per gene (rows) (dge_matrix.txt)
  2. A flatfile with per UMI information (umigroups.txt.gz)

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Organize single cell libraries

First designate the libraries and the cells that were resampled.

cells <- list(
  mouse_human_cell_pulldown = c("GACGTTAGTGCCTGTG",
                                "CTGATCCCATGACGGA"))

libs <- c(
  "original_10x",
  "mouse_human_cell_pulldown")

bc_metadat <- read_tsv(file.path(data_dir, 
                         "lna_control", 
                         "fastq",
                         "original", 
                         "barcodes_from_10x_run.txt"),
                         col_names = c("cell_id", "barcode_10x")) 

## original library to compare against
reflib <- "original_10x"
resampled_libs <- c("mouse_human_cell_pulldown")

## reference resampled lib for resampled vs control plots
resampled_lib <- "mouse_human_cell_pulldown"

## pretty name for libraries
lib_names = c(
  original_10x = "Original Library",
  mouse_human_cell_pulldown = "Resampled Library"
)

## pretty names for cells

cell_names = c(
  "GACGTTAGTGCCTGTG-1" = "Mouse Cell",
  "CTGATCCCATGACGGA-1" = "Human Cell")

Load and organize a table for each library of read counts per cell per gene, and a table of umi counts per cell per gene.

umis_to_genes <- function(umipath, cells_to_exclude = c("Cell_unmatched")){
  umis <- read_tsv(umipath,
                   col_names = c("barcode_10x", 
                                 "umi_molecule", 
                                 "count")) %>% 
    filter(barcode_10x != cells_to_exclude)
  
  mol_fields <- str_count(umis$umi_molecule[1], "::")
  
  if(mol_fields == 2 ){
    umis <- separate(umis, umi_molecule, 
                     into = c("seq", "genome", "gene"),
                     sep = "::") %>% 
      mutate(gene = str_c(genome, "::", gene))
  } else if (mol_fields == 1){
    umis <- separate(umis, umi_molecule, 
                     into = c("seq", "gene"),
                     sep = "::")
  } else {
    stop("separator :: missing from umi_molecule field")
  }
  
  reads <- select(umis, 
                  barcode_10x, 
                  gene,
                  count)
  
  reads <- group_by(reads, 
                   barcode_10x, gene) %>% 
    summarize(counts = sum(count))
  
  reads <- spread(reads, barcode_10x, counts, 
                  fill = 0L)
  
  reads
}

## read in umigroups flat file with read counts per umi per gene per cell
## expand out to a read count matrix
umipaths <- file.path(data_dir, 
                      libs, 
                      "umis",
                      "umigroups.txt.gz")
read_dat <- map(umipaths, 
                ~umis_to_genes(.))
names(read_dat) <- libs

## read in umi_tools count table with umi counts per gene per cell
## drop rows with 0 counts
umi_dat <- map(libs, 
                ~read_tsv(file.path(data_dir, 
                          .x,
                          "dgematrix",
                          "dge_matrix.txt")) %>% 
                 select(-matches("Cell_unmatched")) %>% 
                 .[rowSums(.[, -1]) > 0, ])

names(umi_dat) <- libs

# add in cell info, including info for the original sample
cell_obj_mdata <- map(c(cells[1], cells), 
                      ~mutate(bc_metadat, 
                              resampled = ifelse(barcode_10x %in% .x,
                                                  TRUE,
                                                  FALSE)))
names(cell_obj_mdata) <- libs

Next organize these tables into simple classes called resampled-sets to keep track of each experiment’s relavant raw, processed, and meta data.

#' simple class to hold info for each experiment
create_sc_obj <- function(umi_df,
                          read_df,
                          cell_mdata_df){
  x <- list()
  class(x) <- "resampled-set"
  x$umis <- umi_df
  x$reads <- read_df
  x$meta_dat <- cell_mdata_df
  return(x)
}

sc_objs <- list(umi_dat, read_dat, cell_obj_mdata)
sc_objs <- pmap(sc_objs, create_sc_obj)

rm(umi_dat)
rm(read_dat)

Next perform basic processing. 1) generate separate objects to store sparse matrices of umi and read counts. 2) normalize read and umi count data by total library size (sum of all read or umi counts for all cells in the experiment) and report as Reads per million or UMIs per million. 3) Compute per cell metrics (read and umi counts, sequencing saturation)

tidy_to_matrix <- function(df){
   df <- as.data.frame(df)
   rownames(df) <- df[, 1]
   df[, 1] <- NULL
   mat <- as.matrix(df)
   mat <- as(mat, "sparseMatrix")   
   return(mat)
}

#' keep both tidy and matrix objs
generate_matrices <- function(sc_obj){
  sc_obj$umi_matrix <- tidy_to_matrix(sc_obj$umis)
  sc_obj$read_matrix <- tidy_to_matrix(sc_obj$reads)
  sc_obj
}

#' normalize by library size (Reads per Million)
norm_libsize <- function(sc_obj){
  sc_obj$norm_umi <- 1e6 * sweep(sc_obj$umi_matrix, 2, 
                                 sum(as.vector(sc_obj$umi_matrix)), "/")
  sc_obj$norm_reads <- 1e6 * sweep(sc_obj$read_matrix, 2, 
                                   sum(as.vector(sc_obj$read_matrix)), "/")
  sc_obj
}

add_metadata <- function(sc_obj, dat){
  if (is.vector(dat)){
    new_colname <- deparse(substitute(dat))
    df <- data_frame(!!new_colname := dat)
    df[[new_colname]] <- dat
    df[["cell_id"]] <- names(dat)
    sc_obj$meta_dat <- left_join(sc_obj$meta_dat,
                                 df,
                                 by = "cell_id")
    
  } else if (is.data.frame(dat)) {
    sc_obj$meta_dat <- left_join(sc_obj$meta_dat,
                                 dat,
                                 by = "cell_id")
  }
  sc_obj
}

compute_summaries <- function(sc_obj){
  ## raw counts
  total_umis <- colSums(sc_obj$umi_matrix)
  names(total_umis) <- colnames(sc_obj$umi_matrix)
  total_reads <- colSums(sc_obj$read_matrix)
  names(total_reads) <- colnames(sc_obj$read_matrix)
  
  ## norm counts
  norm_total_umis <- colSums(sc_obj$norm_umi)
  names(norm_total_umis) <- colnames(sc_obj$norm_umi)
  norm_total_reads <- colSums(sc_obj$norm_reads)
  names(norm_total_reads) <- colnames(sc_obj$norm_reads)
    
  sc_obj <- add_metadata(sc_obj, total_umis)
  sc_obj <- add_metadata(sc_obj, total_reads)
  sc_obj <- add_metadata(sc_obj, norm_total_umis)
  sc_obj <- add_metadata(sc_obj, norm_total_reads)
  
  ## compute cDNA duplication rate 
  sc_obj$meta_dat$cDNA_duplication <- 1 - (sc_obj$meta_dat$total_umis /
                                             sc_obj$meta_dat$total_reads)
  
  sc_obj
}

sc_objs <- map(sc_objs, generate_matrices)
sc_objs <- map(sc_objs, norm_libsize)
sc_objs <- map(sc_objs, compute_summaries)

Compute enrichment of reads/umis over the original library.

sc_objs <- map(sc_objs,
    function(sub_dat){
      og_counts <- select(sc_objs[[reflib]]$meta_dat,
                          og_total_reads = total_reads,
                          og_total_umis = total_umis,
                          og_norm_total_umis = norm_total_umis,
                          og_norm_total_reads = norm_total_reads,
                          og_cDNA_duplication = cDNA_duplication,
                          cell_id)
      sub_dat$meta_dat <- left_join(sub_dat$meta_dat,
                         og_counts, 
                         by = "cell_id")
      
      sub_dat$meta_dat <- mutate(sub_dat$meta_dat,
                                 read_proportion = log2( total_reads / og_total_reads),
                                 umi_proportion = log2( total_umis / og_total_umis),
                                 norm_read_proportion = log2( norm_total_reads /
                                                                og_norm_total_reads),
                                 norm_umi_proportion = log2( norm_total_umis /
                                                               og_norm_total_umis))
      sub_dat
    })

plot cDNA duplication rate

sc_metadat <- map(sc_objs, ~.x$meta_dat) %>% 
  bind_rows(.id = "library") %>% 
  mutate(library = factor(library, levels = libs)) %>% 
  arrange(resampled)

plt <- ggplot(sc_metadat, aes(total_umis, cDNA_duplication)) +
  geom_point(aes(color = resampled), size = 0.5) +
  labs(x = expression(paste("# of UMIs")),
       y = "Sequencing saturation") + 
  scale_x_log10(labels = scales::comma) +
  scale_color_manual(values = color_palette) +
  facet_wrap(~library,
             labeller = labeller(library = lib_names)) +
  theme_cowplot(font_size = 16, line_size = 1)  +
  theme(legend.position = "top",
        plot.margin = unit(c(5.5, 20.5, 5.5, 5.5), 
                           "points")) 

plt
Note the high level of sequencing saturation (0 = no-duplication, 1 = all duplicates) in the original library. Also note that the libraries tend to have higher saturatioin rates, after subsampling.

Note the high level of sequencing saturation (0 = no-duplication, 1 = all duplicates) in the original library. Also note that the libraries tend to have higher saturatioin rates, after subsampling.

save_plot("cDNA_duplication.pdf", plt, 
          base_aspect_ratio = 1.6)

plot read and umi counts per library

global_plot_theme <- theme(
        legend.position = "top",
        legend.text = element_text(size = 10),
        strip.text = element_text(size = 8))

resampled_metadat <- filter(sc_metadat,
                             library != reflib) %>% 
  mutate(library = factor(library, 
                          levels = resampled_libs))

unnorm_plt <- ggplot(resampled_metadat, 
                     aes(og_total_reads / 3, total_reads / 3, colour = resampled)) + 
  geom_point(size = 0.5) + 
  geom_abline(slope = 1) +
  facet_wrap(~library, nrow = 1) + 
  coord_fixed() +
  xlab("original library\nreads count (Thousands)") +
  ylab("resampled library\nreads count (Thousands)") +
 # ggtitle("Raw reads associated with each cell barcode") +
  scale_colour_manual(name = "resampled:",
                      values = color_palette) +
  theme_cowplot(font_size = 10, line_size = .5) +
  global_plot_theme

norm_plt <- ggplot(resampled_metadat, aes(og_norm_total_reads / 1e3, norm_total_reads / 1e3, colour = resampled)) + 
  geom_point(size = 0.5) + 
  geom_abline(slope = 1) + 
  facet_wrap(~library, nrow = 1) + 
  xlab("original library\nRPM (Thousands)") +
  ylab("resampled library\nRPM (Thousands)") +
  scale_colour_manual(name = "resampled:",
                      values = color_palette) +
  theme_cowplot(font_size = 10, line_size = 0.5) +
  theme(aspect.ratio = 1) + 
  global_plot_theme

unnorm_umi_plt <- ggplot(resampled_metadat, 
                         aes(og_total_umis / 1e3, total_umis / 1e3, colour = resampled)) + 
  geom_point(size = 0.5) + 
  geom_abline(slope = 1) +
  coord_fixed() +
  facet_wrap(~library, nrow = 1) + 
  xlab("original library\nUMI count (Thousands)") +
  ylab("resampled library\nUMI count (Thousands)") +
  scale_colour_manual(name = "resampled:",
                      values = color_palette) +
  theme_cowplot(font_size = 10, line_size = .5) +
  global_plot_theme

norm_umi_plt <- ggplot(resampled_metadat, 
                       aes(og_norm_total_umis / 1e3, norm_total_umis / 1e3, colour = resampled)) + 
  geom_point(size = 0.5) + 
  geom_abline(slope = 1) + 
  facet_wrap(~library, nrow = 1) + 
  xlab("original library\nUMI normalized RPM (Thousands)") +
  ylab("resampled library\nUMIs per Million (Thousands)") +
  scale_colour_manual(name = "resampled:",
                      values = color_palette) +
  theme_cowplot(font_size = 10, line_size = 0.5) +
  theme(aspect.ratio = 1) + 
  global_plot_theme

plt <- plot_grid(unnorm_plt, norm_plt, unnorm_umi_plt, norm_umi_plt, 
                 labels = "AUTO",
                 align = 'hv')
plt

save_plot("reads_per_barcode_scatterplots.pdf", plt, base_width = 8 )

Plot enrichment of reads/umis

read <- ggplot(resampled_metadat, 
       aes(cell_id, norm_read_proportion, colour = resampled)) + 
  geom_point() + 
  labs(x = "Cell", 
       y = expression(paste( " Log"[2], " normalized reads ", frac("resampled", "original")))) +
  scale_colour_manual(name = "resampled:", values = color_palette) +
  facet_wrap(~library, nrow = 1) + 
  theme_cowplot(font_size = 16, line_size = 1) +
  theme(axis.text.x = element_blank(),
        legend.position = "top",
        legend.text = element_text(size = 12))

umi <- ggplot(resampled_metadat, 
       aes(cell_id, norm_umi_proportion, colour = resampled)) + 
  geom_point() + 
  labs(x = "Cell", 
       y = expression(paste( "Log"[2], " normalized UMIs ", frac("resampled", "original")))) +
  scale_colour_manual(name = "resampled:", values = color_palette) +
  facet_wrap(~library, nrow = 1) + 
  theme_cowplot(font_size = 16, line_size = 1) +
  theme(axis.text.x = element_blank(),
        legend.position = "top",
        legend.text = element_text(size = 12))

plt <- plot_grid(read, umi,
                 labels = "AUTO",
                 align = 'hv')
plt

ggsave("reads_umi_ratio_per_barcode_normalized.pdf", width = 8, height = 5)



umi_plots <- map(split(resampled_metadat, resampled_metadat$library),
  function(x){
    ggplot(x, 
       aes(og_total_umis, norm_umi_proportion, colour = resampled)) + 
  geom_point(size = 0.5) + 
  geom_hline(aes(yintercept = 0), 
             linetype ="dashed", 
             color = "darkgrey") + 
  labs(x = "Abundance in original library\n (UMIs)", 
       y = expression(paste( "Log"[2], " UMIs ", 
                             frac("resampled", "original")))) +
  scale_x_continuous(labels = scales::comma) +      
  scale_colour_manual(name = "resampled:", values = color_palette) +
 # facet_wrap(~library, nrow = 1) + 
  theme_cowplot(font_size = 16, line_size = 1) +
  theme(legend.position = "top",
        legend.text = element_text(size = 12))})

plt <- plot_grid(plotlist = umi_plots, nrow = 1)
save_plot("umi_ratio_MA.pdf", plt)
ggplot(resampled_metadat, aes(resampled, 
                read_proportion, fill = resampled)) + 
  geom_boxplot(coef = Inf) + 
  facet_wrap(~library, nrow = 1) +
  xlab("Selected for Reamplification") +
  ylab(expression(paste( "Log"[2], " Reads ", frac("resampled", "original")))) +
  scale_fill_manual(name = "resampled:",
                    values = color_palette) +
  theme_cowplot(font_size = 16, line_size = 0.5) +
  theme(
    axis.title.x = element_blank(),
    axis.text.x = element_blank(),
    legend.position = "top",
    legend.text = element_text(size = 12)
  )

ggsave("reads_ratio_per_barcode_boxplot.pdf", width = 6, height = 5)

ggplot(resampled_metadat, aes(resampled, 
                umi_proportion, fill = resampled)) + 
  geom_boxplot(coef = Inf) + 
  facet_wrap(~library, nrow = 1) +
  xlab("Selected for Reamplification") +
  ylab(expression(paste( "Log"[2], " UMIs ", frac("resampled", "original")))) +
  scale_fill_manual(name = "resampled:",
                    values = color_palette) +
  theme_cowplot(font_size = 16, line_size = 0.5) +
  theme(
    axis.title.x = element_blank(),
    axis.text.x = element_blank(),
    legend.position = "top",
    legend.text = element_text(size = 12)
  )

ggsave("umi_ratio_per_barcode_boxplot.pdf", width = 6, height = 5)

ggplot(resampled_metadat, aes(resampled, 
                norm_read_proportion, fill = resampled)) + 
  geom_boxplot(coef = Inf) + 
  facet_wrap(~library, nrow = 1) +
  xlab("Selected for Reamplification") +
  ylab(expression(paste( "Log"[2], " normalized Reads ", frac("resampled", "original")))) +
  scale_fill_manual(name = "resampled:",
                    values = color_palette) +
  theme_cowplot(font_size = 16, line_size = 0.5) +
  theme(
    axis.title.x = element_blank(),
    axis.text.x = element_blank(),
    legend.position = "top",
    legend.text = element_text(size = 12)
  )

ggsave("norm_reads_ratio_per_barcode_boxplot.pdf", width = 6, height = 5)

ggplot(resampled_metadat, aes(resampled, 
                norm_umi_proportion, fill = resampled)) + 
  geom_boxplot(coef = Inf) + 
  facet_wrap(~library, nrow = 1) +
  xlab("Selected for Reamplification") +
  ylab(expression(paste( "Log"[2], " normalized UMIs ", frac("resampled", "original")))) +
  scale_fill_manual(name = "resampled:",
                    values = color_palette) +
  theme_cowplot(font_size = 16, line_size = 0.5) +
  theme(
    axis.title.x = element_blank(),
    axis.text.x = element_blank(),
    legend.position = "top",
    legend.text = element_text(size = 12)
  )

ggsave("norm_umi_ratio_per_barcode_boxplot.pdf", width = 6, height = 5)
dat <- group_by(resampled_metadat, library) %>%
  filter(library != reflib) %>% 
  mutate(total_new = sum(total_reads, na.rm = T), 
         total_old = sum(og_total_reads, na.rm = T))

dat_group <- group_by(dat, library, resampled) %>% 
  summarize(total_new = sum(total_reads, 
                            na.rm = T) / unique(total_new), 
            total_old = sum(og_total_reads, 
                            na.rm = T) / unique(total_old)) %>% 
  gather(lib, percent_lib, -library, -resampled ) %>%
  mutate(lib = factor(lib, levels = c("total_old", "total_new"), 
                      labels = c("original\nlibrary", "resampled\nlibrary")))

ggplot(dat_group, aes(lib, percent_lib, fill = resampled)) + 
  geom_bar(stat = "identity") + 
  ylab("Fraction of\n Reads Assigned") +
  scale_fill_manual(name = "resampled:",
                    values = color_palette) +
  facet_wrap(~library) + 
  theme_cowplot(font_size = 16, line_size = 1) +
  theme(
    axis.title.x = element_blank(),
    axis.text.x = element_text(angle = 90, hjust = 0.5, vjust = 0.5),
    legend.position = "top",
    legend.text = element_text(size = 16)
  )

ggsave("proportion_reads_all_barcode_barplot.pdf", width = 7, height = 7)

dat_group %>% 
 rename(Method = library) %>% 
  spread(lib, percent_lib) %>% 
  mutate(`Targeted Library Read Fold-Enrichment` = `resampled\nlibrary` / `original\nlibrary`) %>%
  filter(resampled == T) %>% 
  select(Method, `Targeted Library Read Fold-Enrichment`)

Compare species

add_species_counts <- function(sc_obj, 
                               mouse_gene_prefix = "mm38::",
                               human_gene_prefix = "hg38::"){
  
  ## get mouse and human reads
  g_ids <- rownames(sc_obj$read_matrix)
  mouse_ids <- str_subset(g_ids, str_c("^", mouse_gene_prefix))
  human_ids <- str_subset(g_ids, str_c("^", human_gene_prefix))
  
  mouse_reads = colSums(sc_obj$read_matrix[mouse_ids, ])
  human_reads = colSums(sc_obj$read_matrix[human_ids, ])
  
  ## get mouse and human UMIs
  g_ids <- rownames(sc_obj$umi_matrix)
  mouse_ids <- str_subset(g_ids, str_c("^", mouse_gene_prefix))
  human_ids <- str_subset(g_ids, str_c("^", human_gene_prefix))
  
  mouse_umis = colSums(sc_obj$umi_matrix[mouse_ids, ])
  human_umis = colSums(sc_obj$umi_matrix[human_ids, ])
  
  ## get norm counts for reads 
  g_ids <- rownames(sc_obj$norm_reads)
  mouse_ids <- str_subset(g_ids, str_c("^", mouse_gene_prefix))
  human_ids <- str_subset(g_ids, str_c("^", human_gene_prefix))
  
  norm_human_reads <- colSums(sc_obj$norm_reads[human_ids, ])
  norm_mouse_reads <- colSums(sc_obj$norm_reads[mouse_ids, ])
  
  ## get norm counts for umis
  g_ids <- rownames(sc_obj$norm_umi)
  mouse_ids <- str_subset(g_ids, str_c("^", mouse_gene_prefix))
  human_ids <- str_subset(g_ids, str_c("^", human_gene_prefix))
  
  norm_human_umis <- colSums(sc_obj$norm_umi[human_ids, ])
  norm_mouse_umis <- colSums(sc_obj$norm_umi[mouse_ids, ])
  
  sc_obj <- add_metadata(sc_obj, human_reads)
  sc_obj <- add_metadata(sc_obj, mouse_reads)
  sc_obj <- add_metadata(sc_obj, human_umis)
  sc_obj <- add_metadata(sc_obj, mouse_umis)
  sc_obj <- add_metadata(sc_obj, norm_human_reads)
  sc_obj <- add_metadata(sc_obj, norm_mouse_reads)
  sc_obj <- add_metadata(sc_obj, norm_human_umis)
  sc_obj <- add_metadata(sc_obj, norm_mouse_umis)
  
  ## make sure mouse + human == total
  stopifnot(all(sc_obj$meta_dat$total_reads == sc_obj$meta_dat$mouse_reads + 
                                               sc_obj$meta_dat$human_reads, na.rm = T))
  
  stopifnot(all(sc_obj$meta_dat$total_umis == sc_obj$meta_dat$mouse_umis + 
                                               sc_obj$meta_dat$human_umis, na.rm = T))
  ## check floating point totals
  tol <- 1e-5
  reads_check <- all(abs(sc_obj$meta_dat$norm_total_reads - (sc_obj$meta_dat$norm_mouse_reads + 
                                               sc_obj$meta_dat$norm_human_reads)) <= tol, na.rm = T)
  stopifnot(reads_check)
  
  umis_check <- all(abs(sc_obj$meta_dat$norm_total_umis - (sc_obj$meta_dat$norm_mouse_umis + 
                                               sc_obj$meta_dat$norm_human_umis)) <= tol, na.rm = T)
  stopifnot(umis_check)
  ## calculate species purity (human / human + mouse)
  sc_obj$meta_dat <- mutate(sc_obj$meta_dat, 
                            purity_reads = human_reads / (human_reads + mouse_reads),
                            purity_umis = human_umis / (human_umis + mouse_umis))
  sc_obj
}

sc_objs <- map(sc_objs, add_species_counts)
## add in metadat columns for original mouse and original human data
sc_objs <- map(sc_objs,
    function(sub_dat){
      og_dat <- select(sc_objs[[reflib]]$meta_dat,
                          cell_id,
                          str_subset(colnames(sc_objs[[reflib]]$meta_dat),
                                     "human|mouse|purity"))
                          
      cols <- colnames(og_dat)
      new_cols <- c("cell_id", str_c("og_", cols[2:length(cols)]))
      colnames(og_dat) <- new_cols
      
      sub_dat$meta_dat <- left_join(sub_dat$meta_dat,
                         og_dat, 
                         by = "cell_id")
      
      sub_dat$meta_dat <- mutate(sub_dat$meta_dat,
                                 norm_human_read_proportion = log2( norm_human_reads /
                                                                og_norm_human_reads),
                                 norm_human_umi_proportion = log2( norm_human_umis /
                                                               og_norm_human_umis),
                                 norm_mouse_read_proportion = log2( norm_mouse_reads /
                                                                og_norm_mouse_reads),
                                 norm_mouse_umi_proportion = log2( norm_mouse_umis /
                                                               og_norm_mouse_umis))
      
      sub_dat
    })


resampled_metadat <- map(sc_objs, ~.x$meta_dat) %>% 
  bind_rows(.id = "library") %>% 
  mutate(library = factor(library, 
                          levels = libs)) %>% 
  arrange(resampled)
read_plots <- map(split(resampled_metadat, resampled_metadat$library),
  function(x){
  ggplot(x, 
         aes(human_reads / 1e3, mouse_reads / 1e3, 
                               colour = resampled)) +
  geom_point(size = 0.5) + 
  facet_wrap(~library, nrow = 1, 
             scales = "free", 
             labeller = labeller(library = lib_names)) + 
  xlab("Human reads (Thousands)") +
  ylab("Mouse reads (Thousands)") +
  scale_colour_manual(name = "resampled:",
                      values = color_palette) +
  theme_cowplot(font_size = 16, line_size = 1) +
  theme(legend.position = "top",
        legend.text = element_text(size = 12))
  })
plt <- plot_grid(plotlist = read_plots,
                 nrow = 1)
save_plot("read_scatterplot_human_mouse.pdf", 
          plt, ncol = 3, nrow = 1,
          base_width = 4, base_height = 4)

umi_plots <- map(split(resampled_metadat, resampled_metadat$library),
  function(x){
  ggplot(x,
       aes(human_umis, 
           mouse_umis, 
           colour = resampled)) +
  geom_point(size = 0.5) + 
  facet_wrap(~library, nrow = 1, 
             scales = "free",
             labeller = labeller(library = lib_names)) + 
  xlab("Human UMIs") +
  ylab("Mouse UMIs") +
  scale_x_continuous(labels = scales::comma) + 
  scale_y_continuous(labels = scales::comma) +
  scale_colour_manual(name = "resampled:",
                      values = color_palette) +
  theme_cowplot(font_size = 16, line_size = 1) +
  theme(legend.position = "top",
        legend.text = element_text(size = 12))
  })
plt <- plot_grid(plotlist = umi_plots,
                 nrow = 1)
save_plot("umi_scatterplot_human_mouse.pdf", 
          plt, ncol = 2, nrow = 1,
          base_width = 4, base_height = 4)

Genes detected

## compute per gene or per gene/umi combo enrichment
detected_molecules <- function(sc_obj, molecule = "gene"){
  umis <- sc_obj$umi_matrix
  if (molecule == "gene"){
    human_mat <- umis[str_detect(rownames(umis), "hg38::"), ]
    mouse_mat <- umis[str_detect(rownames(umis), "mm38::"), ]
    n_human_genes <- colSums(human_mat > 0)
    n_mouse_genes <- colSums(mouse_mat > 0)
    out_mdat <- data_frame(cell_id = colnames(umis),
      n_human_genes = n_human_genes,
      n_mouse_genes = n_mouse_genes)
    sc_obj <- add_metadata(sc_obj, out_mdat)
    }
}
sc_objs <- map(sc_objs, ~detected_molecules(.x))
resampled_metadat <- map(sc_objs, ~.x$meta_dat) %>% 
  bind_rows(.id = "library") %>% 
   mutate(library = factor(library, 
                          levels = libs))

og_genes <- filter(resampled_metadat, 
                   library == reflib) %>% 
  dplyr::select(cell_id, 
                og_human_genes = n_human_genes, 
                og_mouse_genes = n_mouse_genes)

resampled_metadat <- left_join(resampled_metadat, 
                                og_genes,
                                by = "cell_id")

ggplot(resampled_metadat, aes(n_human_genes, 
                               og_human_genes, colour = resampled)) + 
  geom_point() + 
  ylab("resampled_genes") +
  xlab("original_genes") + 
  scale_colour_manual(name =  "resampled:", values = color_palette) +
  facet_wrap(~library, nrow = 1) + 
  theme_cowplot(font_size = 16, line_size = 1) +
  theme(
    legend.position = "top",
    legend.text = element_text(size = 16)
  )

ggplot(resampled_metadat, aes(n_mouse_genes, 
                               og_mouse_genes, colour = resampled)) + 
  geom_point() + 
  ylab("resampled_genes") +
  xlab("original_genes") + 
  scale_colour_manual(name =  "resampled:", values = color_palette) +
  facet_wrap(~library, nrow = 1) + 
  theme_cowplot(font_size = 16, line_size = 1) +
  theme(
    legend.position = "top",
    legend.text = element_text(size = 16)
  )

Parse out new versus previously identified genes

calc_gene_sensitivity <- function(sc_obj, 
                                  type = "umi",
                                  mouse_gene_prefix = "mm38::",
                                  human_gene_prefix = "hg38::"){
  
  if (type == "umi"){
    count_matrix <- sc_obj$umi_matrix
  } else {
    count_matrix <- sc_obj$read_matrix
  }
  # generate list named with barcode of each detected gene and 
  # respective read/umi count
  genes_detected <- apply(count_matrix, 2, function(x) x[x > 0])
  sc_obj$genes_detected <- genes_detected
  sc_obj
}

sc_objs <- map(sc_objs, calc_gene_sensitivity)
sc_objs <- map(sc_objs, 
           function(x){
             og_genes <- sc_objs[[reflib]]$genes_detected
             sub_genes <- x$genes_detected
             
             # subset list of cell barcodes to the same as the og experiment
             # and also reorders the barcodes to match
             sub_genes <- sub_genes[names(og_genes)]
             
             if(length(sub_genes) != length(og_genes)){
               stop("barcode lengths not the same")
             }
             shared_genes <- map2(sub_genes, 
                                  og_genes,
                                  ~intersect(names(.x),
                                             names(.y)))
             new_genes <- map2(sub_genes,
                               og_genes,
                               ~setdiff(names(.x),
                                        names(.y)))
             
             not_recovered_genes <- map2(og_genes,
                                         sub_genes,
                                         ~setdiff(names(.x),
                                                  names(.y)))
             x$shared_genes <- shared_genes
             x$new_genes <- new_genes
             x$not_recovered_genes <- not_recovered_genes
             return(x)
             })

## add gene recovery info to meta data table
sc_objs <- map(sc_objs, 
           function(x){
             shared_genes <- map2_dfr(x$shared_genes, 
                                names(x$shared_genes),
                                function(x, y){
                                  mouse <- sum(str_detect(x, "^mm38::")) ;
                                  human <- sum(str_detect(x, "^hg38::")) ;
                                  data_frame(cell_id = y,
                                            mouse_shared_genes = mouse,
                                            human_shared_genes = human,
                                            shared_genes = mouse + human)
                                 })
             
             not_recovered_genes <- map2_dfr(x$not_recovered_genes, 
                                names(x$not_recovered_genes),
                                function(x, y){
                                  mouse <- sum(str_detect(x, "^mm38::")) ;
                                  human <- sum(str_detect(x, "^hg38::")) ;
                                  data_frame(cell_id = y,
                                            mouse_not_recovered_genes = mouse,
                                            human_not_recovered_genes = human,
                                            not_recovered_genes = mouse + human)
                                 })
             
             new_genes <- map2_dfr(x$new_genes, 
                                names(x$new_genes),
                                function(x, y){
                                  mouse <- sum(str_detect(x, "^mm38::")) ;
                                  human <- sum(str_detect(x, "^hg38::")) ;
                                  data_frame(cell_id = y,
                                            mouse_new_genes = mouse,
                                            human_new_genes = human,
                                            new_genes = mouse + human)
                                 })
             gene_mdata <- left_join(shared_genes,
                                     not_recovered_genes,
                                     by = "cell_id") %>% 
               left_join(., new_genes, by = "cell_id")
             
             x <- add_metadata(x, gene_mdata)
             x
           })

resampled_metadat <- map(sc_objs, ~.x$meta_dat) %>% 
  bind_rows(.id = "library") %>% 
   mutate(library = factor(library, 
                          levels = libs)) %>% 
  arrange(resampled)
genes_recovered <- resampled_metadat %>% 
  dplyr::filter(library != reflib) %>% 
  dplyr::select(cell_id, 
                library,
                resampled,
                shared_genes,
                not_recovered_genes, 
                new_genes)

genes_recovered <- gather(genes_recovered, 
                          key = type, value = count, 
                          -cell_id, -resampled, -library)
genes_recovered <- mutate(genes_recovered,
                          type = str_replace_all(type, "_", "\n"))

plt <- ggplot(genes_recovered, 
       aes(cell_id, count)) +
  geom_point(aes(color = resampled),
             size = 0.6,
             alpha = 0.75) +
  facet_grid(type ~ library) +
  theme(axis.text.x = element_blank(),
        axis.title.x = element_blank(),
        strip.text.y = element_text(size = 12,
                                    margin = margin(0,0.2,0,0.2, "cm"))) +
  scale_color_manual(values = color_palette)

plt 

save_plot("new_genes_detected.pdf", plt, base_width = 8, base_height = 8)

targeted <- genes_recovered %>% 
  dplyr::filter(library == resampled_lib,
                resampled) 
targeted
plt_dat <- genes_recovered %>% 
  dplyr::filter(library != resampled_lib,
                resampled) %>% 
  group_by(type) %>% 
  summarize(count = mean(count)) %>% 
  mutate(cell_id = "Not Targeted Barcodes") %>% 
  bind_rows(targeted, .) %>% 
  filter(type == "new\ngenes")


plt <- ggplot(plt_dat, 
       aes(cell_id, count)) +
  geom_bar(aes(fill = cell_id),
           stat = "identity") +
  labs(y = "Newly detected genes") + 
  theme(axis.title.x = element_blank(),
        axis.text.x = element_text(angle = 45,
                                   hjust = 1),
        legend.position = "none") +
  scale_fill_brewer(palette = "Set1")

save_plot("new_genes_barplot.pdf", plt, 
          base_width = 3.6, base_height = 5)


## species specific

genes_recovered <- resampled_metadat %>% 
     dplyr::filter(library %in% resampled_libs) %>% 
     select(cell_id, resampled, purity_reads, ends_with("genes")) %>% 
  mutate(species = ifelse(purity_reads > 0.80,
                          "human",
                          ifelse(purity_reads < 0.20, 
                                 "mouse",
                                 "mixed"))) %>% 
  filter(species != "mixed") %>% 
  mutate(shared_genes_species = ifelse(species == "human",
                                          human_shared_genes,
                                          mouse_shared_genes),
         new_genes_species = ifelse(species == "human",
                                    human_new_genes,
                                    mouse_new_genes),
         not_recovered_genes_species = ifelse(species == "human",
                                              human_not_recovered_genes,
                                              mouse_not_recovered_genes)) %>% 
  select(cell_id, resampled, ends_with("_species")) %>% 
  gather(key = type, value = count, 
         -cell_id, -resampled) %>% 
   mutate(type = str_replace(type, "_species", "") %>% 
            str_replace_all(., "_", "\n"))

targeted <- genes_recovered %>% 
  dplyr::filter(resampled) 

plt_dat <- genes_recovered %>% 
  dplyr::filter(!resampled) %>% 
  group_by(type) %>% 
  summarize(count = mean(count)) %>% 
  mutate(cell_id = "Not targeted\nbarcodes\n(mean)") %>% 
  bind_rows(targeted, .) %>% 
  mutate(type = ifelse(type == "new\ngenes",
                        "Newly\ndetected\ngenes",
                        ifelse(type == "shared\ngenes",
                               "Previously\ndetected\ngenes",
                               ifelse(type == "not\nrecovered\ngenes",
                                      "Previously\ndetected\ngenes\nnot recovered",
                                      NA))))


plt <- ggplot(plt_dat, 
       aes(cell_id, count)) +
  geom_bar(aes(fill = type),
           stat = "identity") +
  labs(y = "# of genes") + 
  scale_x_discrete(labels = cell_names) + 
  scale_y_continuous(labels = scales::comma) + 
  scale_fill_brewer(palette = "Set1", name = "") +
  guides(fill = guide_legend(override.aes = list(size = 0.25))) +
  theme(axis.title.x = element_blank(),
        axis.text.x = element_text(angle = 45,
                                   hjust = 1),
        legend.position = "top",
        plot.margin = unit(c(5.5, 50.5, 5.5, 5.5), 
                           "points"))
      #  legend.key.size = unit(0.25, "pt")) 

save_plot("new_genes_species_specific_barplot.pdf", plt, 
          base_width = 3.6, base_height = 5) 

Plot expression per cell

MA plots

calc_ma <- function(xmat, ymat, cell = "GCAGTTAAGTGTCCAT"){
  x_rn <- rownames(xmat)
  y_rn <- rownames(ymat)
  xmat <- log2(xmat + 1)
  ymat <- log2(ymat + 1)
  
  rownames(xmat) <- x_rn
  rownames(ymat) <- y_rn
  m <- rowMeans(log2(((2^ymat + 2^xmat) / 2) + 1))
  a <- xmat[, cell] - ymat[, cell]
  data_frame(gene = names(a),
             mean_expression_log2 = m,
             log2_diff = a)
}


genes_to_plot <- rownames(sc_objs[[resampled_lib]]$umi_matrix)
cols <- colnames(sc_objs[[resampled_lib]]$norm_umi)
cell_ids <- str_c(cells[[resampled_lib]], "-1")

## append genes to reference library if necessary
ref_mat <- standardize_rows(sc_objs[[resampled_lib]]$umi_matrix[, cols],
                            sc_objs[[reflib]]$umi_matrix[, cols])

ma_dat <- map(cell_ids,
    ~calc_ma(sc_objs[[resampled_lib]]$umi_matrix[genes_to_plot, cols], 
             ref_mat[genes_to_plot, cols],
             cell = .x))

names(ma_dat) <- cell_ids
ma_dat <- bind_rows(ma_dat, .id = "cell")

plot_ma <- function(df){
  n_up <- filter(df, log2_diff > 0) %>% 
    group_by(cell) %>% 
    summarize(n = n(), n = paste0("up = ", n))
  
  n_down <- filter(df, log2_diff < 0) %>% 
    group_by(cell) %>% 
    summarize(n = n(), n = paste0("down = ", n))
  
  if (nrow(n_down) == 0) {
    n_down = data_frame(cell = df$cell %>% unique(),
                        n = "down = 0")
  }
  plt <- ggplot(df,
         aes(mean_expression_log2,
             log2_diff)) +
    geom_hline(aes(yintercept = 0), linetype = "dashed", colour = "grey") + 
    geom_point(size = 0.25) +
    geom_text(data = n_up, aes(x = max(ma_dat$mean_expression_log2) * 0.9, 
                               y = max(ma_dat$log2_diff) * 1.2, 
                               label = n)) +
    geom_text(data = n_down, aes(x = max(ma_dat$mean_expression_log2) * 0.9, 
                                 y = min(ma_dat$log2_diff) * 1.2, 
                                 label = n)) + 
    facet_wrap(~cell) +
    labs(x = expression(paste("Abundance (log"[2], ")")),
         y = expression(paste(frac("resampled","Original"), " (log"[2], ")")))
  plt
}

plt <- plot_ma(ma_dat)
plt

save_plot("per_cell_MA_plot_all_genes.pdf", plt, base_height = 6)

## Shared genes
ma_dat <- map(cell_ids,
  function(x){
    genes_to_plot <- sc_objs[[resampled_lib]]$shared_genes[[x]]
    calc_ma(sc_objs[[resampled_lib]]$umi_matrix[genes_to_plot,
                                                              cols],
            ref_mat[genes_to_plot, cols],
             cell = x)
})

names(ma_dat) <- cell_ids
ma_dat <- bind_rows(ma_dat, .id = "cell")
plt <- plot_ma(ma_dat)
plt

save_plot("per_cell_MA_plot_shared_genes.pdf", plt, base_height = 6)


## New genes
ma_dat <- map(cell_ids,
  function(x){
    genes_to_plot <- sc_objs[[resampled_lib]]$new_genes[[x]]
    calc_ma(sc_objs[[resampled_lib]]$umi_matrix[genes_to_plot,
                                                              cols],
            ref_mat[genes_to_plot, cols],
             cell = x)
})

names(ma_dat) <- cell_ids
ma_dat <- bind_rows(ma_dat, .id = "cell")
plt <- plot_ma(ma_dat)
plt

save_plot("per_cell_MA_plot_new_genes.pdf", plt, base_height = 6)

Histograms

get_expr <- function(xmat, ymat, cell = "GCAGTTAAGTGTCCAT"){
  xrows <- rownames(xmat)
  xmat <- log2(xmat[, cell] + 1)
  ymat <- log2(ymat[xrows, cell] + 1)
  data_frame(
    gene = xrows,
    resampled = xmat,
    original = ymat) %>% 
    gather(library, 
           Expression, -gene)
}

plot_histogram <- function(df){
  ggplot(df, 
         aes_string("Expression")) +
    geom_density(aes_string(fill = "library"),
                 alpha = 0.66) +
    scale_fill_viridis_d(name = "") +
    facet_wrap(~cell, nrow = 1) +
    theme(legend.position = "top",
          strip.text = element_text(size = 8)) 
}

expr_dat <- map(cell_ids,
  function(x){
    genes_to_plot <- names(sc_objs[[resampled_lib]]$genes_detected[[x]])
    get_expr(sc_objs[[resampled_lib]]$umi_matrix[genes_to_plot,
                                                              cols],
            ref_mat[genes_to_plot, cols],
             cell = x)
})

names(expr_dat) <- cell_ids
expr_dat <- bind_rows(expr_dat, .id = "cell")
expressed_in_resampled_plt <- plot_histogram(expr_dat)
expressed_in_resampled_plt

expr_dat <- map(cell_ids,
  function(x){
    genes_to_plot <- sc_objs[[resampled_lib]]$shared_genes[[x]]
    get_expr(sc_objs[[resampled_lib]]$umi_matrix[genes_to_plot,
                                                              cols],
            ref_mat[genes_to_plot, cols],
             cell = x)
})

names(expr_dat) <- cell_ids
expr_dat <- bind_rows(expr_dat, .id = "cell")
share_genes_plt <- plot_histogram(expr_dat)
share_genes_plt

expr_dat <- map(cell_ids,
  function(x){
    genes_to_plot <- sc_objs[[resampled_lib]]$new_genes[[x]]
    get_expr(sc_objs[[resampled_lib]]$umi_matrix[genes_to_plot,
                                                              cols],
            ref_mat[genes_to_plot, cols],
             cell = x)
})

names(expr_dat) <- cell_ids
expr_dat <- bind_rows(expr_dat, .id = "cell")
new_gene_plt <- plot_histogram(expr_dat)


plts <- list(
  expressed_in_resampled_plt,
  share_genes_plt,
  new_gene_plt
)

plt <- plot_grid(plotlist = plts, ncol = 1)
plt

save_plot("expression_histograms.pdf", plt, 
          ncol = 1, nrow = 3,
          base_height = 4,
          base_aspect_ratio = 2)

scatterplots

get_paired_expr <- function(xmat, ymat, cell = "GCAGTTAAGTGTCCAT"){
  xrows <- rownames(xmat)
  xmat <- log2(xmat[, cell] + 1)
  ymat <- log2(ymat[xrows, cell] + 1)
  data_frame(
    gene = xrows,
    resampled = xmat,
    original = ymat)
}

plot_scatter <- function(df){
  
  ggplot(df, 
         aes_string("original", "resampled")) +
    geom_point(size = 0.5) + 
    geom_abline(aes(slope = 1, intercept = 0)) +
    facet_wrap(~cell, nrow = 1) +
    expand_limits(x = 0, y = 0) +
    coord_fixed() + 
    scale_x_continuous(breaks = scales::pretty_breaks(n = 5)) +
    scale_y_continuous(breaks = scales::pretty_breaks(n = 5)) + 
    theme(legend.position = "top",
          strip.text = element_text(size = 8)) 
}

expr_dat <- map(cell_ids,
  function(x){
    genes_to_plot <- names(sc_objs[[resampled_lib]]$genes_detected[[x]])
    get_paired_expr(sc_objs[[resampled_lib]]$umi_matrix[genes_to_plot,
                                                              cols],
            ref_mat[genes_to_plot, cols],
             cell = x)
})

names(expr_dat) <- cell_ids
expr_dat <- bind_rows(expr_dat, .id = "cell")
expressed_in_resampled_plt <- plot_scatter(expr_dat)
expressed_in_resampled_plt

expr_dat <- map(cell_ids,
  function(x){
    genes_to_plot <- sc_objs[[resampled_lib]]$shared_genes[[x]]
    get_paired_expr(sc_objs[[resampled_lib]]$umi_matrix[genes_to_plot,
                                                              cols],
            ref_mat[genes_to_plot, cols],
             cell = x)
})

names(expr_dat) <- cell_ids
expr_dat <- bind_rows(expr_dat, .id = "cell")
share_genes_plt <- plot_scatter(expr_dat)
share_genes_plt

expr_dat <- map(cell_ids,
  function(x){
    genes_to_plot <- sc_objs[[resampled_lib]]$new_genes[[x]]
    get_paired_expr(sc_objs[[resampled_lib]]$umi_matrix[genes_to_plot,
                                                              cols],
            ref_mat[genes_to_plot, cols],
             cell = x)
})

names(expr_dat) <- cell_ids
expr_dat <- bind_rows(expr_dat, .id = "cell")
new_gene_plt <- ggplot(expr_dat, aes(cell,resampled)) +
  geom_jitter(alpha = 0.55) +
  geom_violin(aes(fill = cell)) +
  ylim(0, max(expr_dat$resampled) * 1.10) + 
  scale_fill_brewer(palette = "Set1") +
    theme(axis.text.x = element_text(angle = 90, 
                                     hjust = 1, vjust = 0.5),
          legend.pos = "none",
          axis.title.x = element_blank()) 

new_gene_plt

plts <- list(
  expressed_in_resampled_plt,
  share_genes_plt
)

plt <- plot_grid(plotlist = plts, ncol = 1)
plt

save_plot("expression_scatterplots.pdf", plt, 
          ncol = 1, nrow = 3,
          base_height = 4,
          base_aspect_ratio = 2)

save_plot("expression_newgenes_violinplots.pdf", new_gene_plt, 
          base_height = 6,
          base_aspect_ratio = 0.5)

Parse out new versus previously identified UMIs

compare_umis <- function(path_to_ctrl,
                         path_to_test,
                         return_summary = F){

  ## umi seqs should be produced by ./get_molecule_info
  ctrl_umi_seqs <- read_tsv(path_to_ctrl,
                   col_names = c("barcode_10x", 
                                 "umi_molecule", 
                                 "count")) %>% 
  filter(barcode_10x != "Cell_unmatched")

  test_umi_seqs <- read_tsv(path_to_test,
                   col_names = c("barcode_10x", 
                                 "umi_molecule", 
                                 "count")) %>% 
  filter(barcode_10x != "Cell_unmatched")

  umi_seqs <- full_join(ctrl_umi_seqs, 
          test_umi_seqs, 
          by = c("barcode_10x", "umi_molecule"))
  
  if (return_summary) {
    umi_seqs %>% 
      mutate(new_umi = ifelse(is.na(count.x) & !is.na(count.y), 
                          1L, 
                          0L),
         not_detected_umi = ifelse(!is.na(count.x) & is.na(count.y),
                                   1L,
                                   0L),
         shared_umi = ifelse(!is.na(count.x) & !is.na(count.y),
                             1L,
                             0L)) %>% 
      group_by(barcode_10x) %>% 
      summarize(new_umis = sum(new_umi),
            not_detected_umis = sum(not_detected_umi),
            shared_umis = sum(shared_umi))
  } else {
    umi_seqs
  }
}

umi_files <- file.path(data_dir, libs, "umis", "umigroups.txt.gz")

umi_summaries <- map(umi_files[2],
                   ~compare_umis(umi_files[1], .x, return_summary = T))

names(umi_summaries) <- umi_files[2] %>% 
  str_split(., "/") %>% 
  map_chr(~.x[7])

umi_summary <- bind_rows(umi_summaries, .id = "library")

umis_recovered <- umi_summary %>% 
  gather(class, count, -barcode_10x, -library) 

## annotate with resampled or not

cell_annot <- data_frame(barcode_10x = c(cells[1], cells),
                         library = libs,
                         resampled = T) %>% 
  unnest()

umis_recovered <- umis_recovered %>% 
  mutate(resampled = ifelse(str_replace(barcode_10x, "-1", "") %in% unlist(cells),
                             T,
                             F)) %>% 
  arrange(resampled)

plt <- ggplot(umis_recovered, 
       aes(barcode_10x, count)) +
  geom_point(aes(colour = resampled),
             size = 0.6,
             alpha = 0.75) +
  facet_grid(library ~ class) +
  theme(axis.text.x = element_blank(),
        axis.title.x = element_blank(),
        strip.text.y = element_text(size = 12,
                                    margin = margin(0,0.2,0,0.2, "cm"))) +
  scale_color_manual(values = color_palette)

plt 

umi_seqs <- map(umi_files[2],
                ~compare_umis(umi_files[1], .x, return_summary = F))

names(umi_seqs) <- umi_files[2] %>% 
  str_split(., "/") %>% 
  map_chr(~.x[7])

new_umis <- map(umi_seqs, 
    ~filter(.x, 
       str_replace(barcode_10x, "-1", "") %in% unlist(cells), 
       !is.na(count.y), 
       is.na(count.x))  %>% 
  separate(umi_molecule, c("seq", "genome", "gene"), sep = "::") %>% 
    select(-starts_with("count"))) 

old_umis <- map(umi_seqs, 
    ~filter(.x, 
       str_replace(barcode_10x, "-1", "") %in% unlist(cells), 
       !is.na(count.x),
       !is.na(count.y))  %>% 
  separate(umi_molecule, c("seq", "genome", "gene"), sep = "::") %>% 
    select(-starts_with("count"))) 


umi_edit_dist <- map2(new_umis,
                      old_umis,
                     ~left_join(.x, .y, 
               by = c("barcode_10x", "genome", "gene")) %>% 
  na.omit() %>% 
  mutate(ed = kentr::get_hamming_pairs(seq.x, seq.y)) %>% 
  group_by(barcode_10x, seq.y, genome, gene) %>% 
  summarize(min_ed = min(ed)) %>% 
  ungroup())

umi_edit_dist <- bind_rows(umi_edit_dist, 
                           .id = "library")

ggplot(umi_edit_dist, aes(barcode_10x, 
                          min_ed)) + 
  geom_boxplot(coef = Inf) +
  facet_wrap(~library, scales = "free_x") +
  labs(y = "Minimum edit distance\noriginal vs. new UMIs") +
  theme(axis.text.x = element_text(angle = 90,
                                   hjust = 1, 
                                   vjust = 0.5),
        axis.title.x = element_blank())

rm(umi_seqs)

tSNE analysis

original library tSNE

library(Seurat)

mat <- sc_objs[[reflib]]$umi_matrix
sobj <- CreateSeuratObject(mat)
sobj <- NormalizeData(sobj)
sobj <- ScaleData(sobj)
sobj <- FindVariableGenes(sobj, do.plot = F, y.cutoff = 0.33)
sobj <- RunPCA(sobj, pc.genes = sobj@var.genes, 
               pcs.compute = 20, 
               do.print = F, seed.use = 20180521)
sobj <- RunTSNE(sobj, dims.use = 1:7, seed.use = 20180521)
sobj <- FindClusters(sobj,
                     dims.use = 1:7, 
                     resolution = 0.6, 
                     print.output = F, 
                     random.seed = 20180521)
plt <- TSNEPlot(sobj, 
                colors.use = c(brewer.pal(12, "Paired"), 
                               brewer.pal(9, "Set1")),
                do.label = T) + 
  labs(title = "NIH3T3 and 293T") +
  theme(legend.position = "none")

plt

save_plot("original_cells_tsne.pdf", plt, 
          base_height = 4.25, base_width = 4.25)

plts <- FeaturePlot(sobj, c("mm38::Malat1", "hg38::MALAT1"), do.return = T)

plt <- plot_grid(plotlist = plts, nrow = 1)
save_plot("original_cells_mouse_human_markers.pdf", plt, 
          base_height = 4.25, base_width = 8.5)

original library tSNE supplemented with resampled barcodes

mat <- sc_objs[[reflib]]$umi_matrix

resampled_ids <- sc_objs[[resampled_lib]]$meta_dat %>% 
  filter(resampled) %>% 
  pull(cell_id)

resampled_mat <- sc_objs[[resampled_lib]]$umi_matrix[, resampled_ids]
colnames(resampled_mat) <- str_c(colnames(resampled_mat), 
                                  "::",
                                  "resampled")

mat <- as.data.frame(as.matrix(mat)) %>%
  rownames_to_column("gene")
resampled_mat <- as.data.frame(as.matrix(resampled_mat)) %>%
  rownames_to_column("gene")

combined_mats <- left_join(mat, resampled_mat, by = c("gene")) 
combined_mats <- as.data.frame(combined_mats) %>% 
  column_to_rownames("gene") %>% 
  as.matrix() %>% 
  as(., "sparseMatrix")   

combined_mats[is.na(combined_mats)] <- 0

sobj <- CreateSeuratObject(combined_mats)

new_ids <- sobj@meta.data %>% 
  rownames_to_column("cell") %>% 
  mutate(resampled = ifelse(str_detect(cell, "resampled"),
                             "resampled",
                             "not resampled"))

resampled_cell_ids <- new_ids[new_ids$resampled == "resampled", "cell"] %>% 
  str_replace("::resampled", "")
 
new_ids <- mutate(new_ids, 
                  resampled = ifelse(cell %in% resampled_cell_ids, 
                                      "original cell",
                                      resampled)) %>% 
  select(cell, resampled) %>% 
  as.data.frame(.) %>% 
  column_to_rownames("cell")

sobj <- AddMetaData(sobj, new_ids)
sobj <- NormalizeData(sobj)
sobj <- ScaleData(sobj)
sobj <- FindVariableGenes(sobj, do.plot = T, y.cutoff = 1)

sobj <- RunPCA(sobj, pc.genes = rownames(sobj@data), 
               pcs.compute = 20, 
               do.print = F, seed.use = 20180522)
sobj <- RunTSNE(sobj, dims.use = 1:7, seed.use = 20180522)
sobj <- FindClusters(sobj, 
                     dims.use = 1:7, 
                     resolution = 0.6, 
                     print.output = F, 
                     random.seed = 20180522)
mdata <- sc_objs$mouse_human_cell_pulldown$meta_dat %>% 
  select(cell_id, resampled, matches("purity_umis"))

mdata_sub <- filter(mdata, resampled) %>% 
  select(cell_id, resampled, proportion_human = purity_umis) %>% 
  mutate(cell_id = str_c(cell_id, "::resampled"),
         proportion_mouse = 1 - proportion_human) 

mdata_ogsub <- filter(mdata, resampled) %>% 
  select(cell_id, resampled, proportion_human = og_purity_umis) %>% 
  mutate(proportion_mouse = 1 - proportion_human) 

mdata_not_sub <- filter(mdata, !resampled) %>% 
  select(cell_id, resampled, proportion_human = og_purity_umis) %>% 
  mutate(proportion_mouse = 1 - proportion_human)

mdata <- bind_rows(list(mdata_sub, mdata_ogsub, mdata_not_sub)) %>% 
  unique() %>% 
  mutate(species = ifelse(proportion_human > 0.90,
                          "Human",
                          ifelse(proportion_mouse > 0.90,
                                 "Mouse",
                                 "Doublet"))) %>% 
  as.data.frame() %>% 
  tibble::column_to_rownames("cell_id") %>% 
  select(-resampled) 


sobj <- AddMetaData(sobj, mdata)
sobj <- SetAllIdent(sobj, "species")

tsne_dat <- GetCellEmbeddings(sobj, reduction.type = "tsne") %>% 
  as.data.frame() %>% 
  tibble::rownames_to_column("cell")

mdata <- sobj@meta.data %>% 
  as.data.frame() %>% 
  tibble::rownames_to_column("cell")

tsne_dat <- left_join(tsne_dat, 
                         mdata,
                         by = "cell")
plt <- ggplot(tsne_dat, 
              aes(tSNE_1, tSNE_2)) + 
  geom_point(aes(color = species), size = 0.1) + 
  scale_color_manual(values = brewer.pal(3, "Paired"),
                     name = "") +
  labs(title = "",
       x = "tSNE 1",
       y = "tSNE 2") +
  theme_cowplot() + 
  guides(color = guide_legend(nrow = 3, 
                              ncol = 1,
                              override.aes = list(size = 4))) + 
  theme(legend.position = c(1.05, 1.05),
        legend.justification = c("right", "top"),
        legend.box.just = "right")  

save_plot("original_resampled_cells_mouse_human.pdf", plt, 
          base_height = 4.25, base_width = 8.5)


og_cell_dat <- tsne_dat %>% 
  filter(cell %in% str_c(cells[[resampled_lib]], "-1")) 

resampled_cell_dat <- tsne_dat %>% 
  filter(cell %in% str_c(cells[[resampled_lib]], 
                                 "-1::resampled")) %>% 
  mutate(cell = str_replace(cell, "::resampled", "")) %>% 
  select(cell, tSNE_1, tSNE_2)

cell_dat <- left_join(og_cell_dat, 
                      resampled_cell_dat, 
                      by = "cell", 
                      suffix = c("", "_resampled"))


resampled_cells <- ggplot(tsne_dat, 
              aes(tSNE_1, tSNE_2)) + 
  geom_point(aes(color = resampled), size = 0.1) + 
  scale_color_manual(values = c("lightgrey",
                                brewer.pal(7, "Set1")[1:2]),
                     name = "",
                     labels = c("not resampled" = "Not Resampled",
                                "original cell" = "Original Cell",
                                "resampled" = "Resampled Cell")) +
  geom_segment(data = cell_dat, 
                               aes(x = tSNE_1,
                                   y = tSNE_2, 
                                   xend = tSNE_1_resampled,
                                   yend = tSNE_2_resampled),
                               linejoin = "mitre",
                               arrow = arrow(length = unit(0.03, "npc"))) + 
  labs(title = "",
       x = "tSNE 1",
       y = "tSNE 2") +
  theme_cowplot() + 
  guides(color = guide_legend(nrow = 3, 
                              ncol = 1,
                              override.aes = list(size = 4))) + 
  theme(legend.position = c(1.05, 1.05),
        legend.justification = c("right", "top"),
        legend.box.just = "right")  

plt <- plot_grid(plt, 
                 resampled_cells,
                 ncol = 2)
plt

save_plot("resampled_tsne.pdf", plt, 
          nrow = 1, ncol = 2,
          base_height = 5.5, base_width = 4.25)
saveRDS(sobj, "rs_sobj.rds")

kNN analysis

Find the k-nearest neighbors in PCA space

## use combined data from above
data.use <- GetCellEmbeddings(object = sobj,
                              reduction.type = "pca",
                              dims.use = 1:7)

## findnearest neighboors using exact search
knn <- RANN::nn2(data.use, k = 5,
                 searchtype = 'standard',
                 eps = 0)

resampled_idxs <- knn$nn.idx[str_detect(rownames(data.use), "::resampled"), ]

nn_ids <- as_data_frame(t(apply(resampled_idxs, 1,
                      function(x)rownames(data.use)[x])))

colnames(nn_ids) <- c("query_cell", 
                      paste0("nearest neighbor ", 1:(ncol(nn_ids) - 1)))

nn_ids